You're reading from TinyML Cookbook - Second Edition

Product type Book

Published in Nov 2023

Publisher Packt

ISBN-13 9781837637362

Pages 664 pages

Edition 2nd Edition

Languages

Concepts

Machine Learning

Author (1):

Gian Marco Iodice

Table of Contents (16) Chapters

Preface

1. Getting Ready to Unlock ML on Microcontrollers

2. Unleashing Your Creativity with Microcontrollers

3. Building a Weather Station with TensorFlow Lite for Microcontrollers

4. Using Edge Impulse and the Arduino Nano to Control LEDs with Voice Commands

5. Recognizing Music Genres with TensorFlow and the Raspberry Pi Pico – Part 1

6. Recognizing Music Genres with TensorFlow and the Raspberry Pi Pico – Part 2

7. Detecting Objects with Edge Impulse Using FOMO on the Raspberry Pi Pico

8. Classifying Desk Objects with TensorFlow and the Arduino Nano

9. Building a Gesture-Based Interface for YouTube Playback with Edge Impulse and the Raspberry Pi Pico

10. Deploying a CIFAR-10 Model for Memory-Constrained Devices with the Zephyr OS on QEMU

11. Running ML Models on Arduino and the Arm Ethos-U55 microNPU Using Apache TVM

12. Enabling Compelling tinyML Solutions with On-Device Learning and scikit-learn on the Arduino Nano and Raspberry Pi Pico

13. Conclusion

14. Other Books You May Enjoy

15. Index

Deploying the TensorFlow Lite for Microcontrollers program on QEMU

The skeleton of our Zephyr Project is ready, so we just need to finalize our application to classify our input test image.

In this recipe, we will see how to implement the tflite-micro application and run the model on the emulated Arm Cortex-M3-based microcontroller.

Getting ready

Most of the ingredients required for this recipe are related to tflite-micro and have already been covered in earlier chapters, such as Chapter 3, Building a Weather Station with TensorFlow Lite for Microcontrollers. Nevertheless, there is one useful feature of this framework that has not been introduced yet, but it can come in handy when we want to reduce the program memory usage of our application drastically. This feature is the tflite::MicroMutableOpResolver, which enables the compilation of a subset of all operators available in tflite-micro.

As shown in the following ML model example, the model is composed of a series...